class_docstr: A simple critic network used in SAC.
code: def __call__(
      self, state: jnp.ndarray, action: jnp.ndarray
  ) -> jnp.ndarray:
      kernel_initializer = jax.nn.initializers.glorot_uniform()
  
      # Preprocess inputs
      a = action.reshape(-1)  # flatten
      x = state.astype(jnp.float32)
      x = x.reshape(-1)  # flatten
      x = jnp.concatenate((x, a))
  
      for _ in range(self.num_layers):
          x = nn.Dense(
              features=self.hidden_units, kernel_init=kernel_initializer
          )(x)
          x = nn.relu(x)
  
      return nn.Dense(features=1, kernel_init=kernel_initializer)(x)
  
imports: import functools
  import operator
  from typing import NamedTuple, Optional, Tuple
  from flax import linen
  import gin
  import jax
  from jax import numpy
  import tensorflow
  from tensorflow_probability.substrates import jax
label_desc: Use image node_id 1 for calling the SACCriticNetwork obj's underlying member method code with example usage: obj.__call__(state, action) and returns: nn